This notebook shows how to preprocess audio files, load a trained model, how to predict pitches and evaluate the estimates.
import os
import sys
basepath = os.path.dirname(os.path.abspath('.'))
sys.path.append(basepath)
import numpy as np
import pandas as pd
import librosa
import libfmp
import matplotlib.pyplot as plt
import IPython.display as ipd
import torch
import torchinfo
import libdl
# CPU / GPU
device = torch.device('cpu')
# device = torch.device('cuda')
fs = 22050
audio_folder = os.path.join(basepath, 'data', 'Schubert_Winterreise', '01_RawData', 'audio_wav')
fn_audio = 'Schubert_D911-23_SC06.wav'
# Load audio
path_audio = os.path.join(audio_folder, fn_audio)
f_audio, fs_load = librosa.load(path_audio, sr=fs)
libfmp.b.plot_signal(f_audio, Fs=fs_load)
ipd.display(ipd.Audio(data=f_audio, rate=fs_load))